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OpenAl's President, Greg Brockman, has shared a four-pillar framework to help users craft effective Al prompts. This approach aims to enhance the clarity and precision of Al-generated responses. The four pillars are:
Define a Clear Goal: A specific objective ensures the Al focuses on relevant details. Instead of asking, “Tell me about cars,” a better prompt would be, “Compare electric and gasoline cars in terms of cost, maintenance, and environmental impact.”
Specify the Return Format: Indicating the desired output format-such as bullet points, tables, or paragraphs-helps streamline responses. For instance, requesting a table for comparisons ensures organized and easy-to-read results.
Adding Warnings: Incorporate instructions to enhance the accuracy of the response. For example, "List the top five safest car brands based on 2024 crash test ratings, excluding outdated information."
Provide Context: Supplying background information or preferences personalizes the response. For example, clarifying that you need a fuel-efficient car for long highway drives can refine recommendations.
By applying this framework, users can guide Al models to deliver more precise, structured, and relevant outputs.
#AI #Automation #WebDev #DigitalMarketing #gregbrockman #openai #promptframework
Define a Clear Goal: A specific objective ensures the Al focuses on relevant details. Instead of asking, “Tell me about cars,” a better prompt would be, “Compare electric and gasoline cars in terms of cost, maintenance, and environmental impact.”
Specify the Return Format: Indicating the desired output format-such as bullet points, tables, or paragraphs-helps streamline responses. For instance, requesting a table for comparisons ensures organized and easy-to-read results.
Adding Warnings: Incorporate instructions to enhance the accuracy of the response. For example, "List the top five safest car brands based on 2024 crash test ratings, excluding outdated information."
Provide Context: Supplying background information or preferences personalizes the response. For example, clarifying that you need a fuel-efficient car for long highway drives can refine recommendations.
By applying this framework, users can guide Al models to deliver more precise, structured, and relevant outputs.
#AI #Automation #WebDev #DigitalMarketing #gregbrockman #openai #promptframework
OpenAl's President, Greg Brockman, has shared a four-pillar framework to help users craft effective Al prompts. This approach aims to enhance the clarity and precision of Al-generated responses. The four pillars are:
✅Define a Clear Goal: A specific objective ensures the Al focuses on relevant details. Instead of asking, “Tell me about cars,” a better prompt would be, “Compare electric and gasoline cars in terms of cost, maintenance, and environmental impact.”
✅Specify the Return Format: Indicating the desired output format-such as bullet points, tables, or paragraphs-helps streamline responses. For instance, requesting a table for comparisons ensures organized and easy-to-read results.
✅Adding Warnings: Incorporate instructions to enhance the accuracy of the response. For example, "List the top five safest car brands based on 2024 crash test ratings, excluding outdated information."
✅Provide Context: Supplying background information or preferences personalizes the response. For example, clarifying that you need a fuel-efficient car for long highway drives can refine recommendations.
By applying this framework, users can guide Al models to deliver more precise, structured, and relevant outputs.
#AI #Automation #WebDev #DigitalMarketing #gregbrockman #openai #promptframework
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